Splitting Datasets
foryourhealth111-pixel/Vibe-Skills
Split datasets into training, validation, and test partitions with the right stratification and temporal rules.
Agent skill
Recsys Artifact Evaluation
by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills
A skill your agent uses when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after…
MITAuto-check passed
Install Recsys Artifact Evaluation
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-artifact-evaluation --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/RecSys-Skills/skills/recsys-artifact-evaluation .claude/skills/recsys-artifact-evaluation && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "recsys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluation into .claude/skills/recsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-artifact-evaluation", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluationType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-artifact-evaluation --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/RecSys-Skills/skills/recsys-artifact-evaluation .agents/skills/recsys-artifact-evaluation && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "recsys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluation into .agents/skills/recsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-artifact-evaluation", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-artifact-evaluation --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/RecSys-Skills/skills/recsys-artifact-evaluation .cursor/skills/recsys-artifact-evaluation && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "recsys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluation into .cursor/skills/recsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-artifact-evaluation", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path RecSys-Skills/skills/recsys-artifact-evaluation--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-artifact-evaluation --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/RecSys-Skills/skills/recsys-artifact-evaluation .gemini/skills/recsys-artifact-evaluation && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "recsys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluation into .gemini/skills/recsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-artifact-evaluation", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-artifact-evaluationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/RecSys-Skills/skills/recsys-artifact-evaluation .github/skills/recsys-artifact-evaluation && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "recsys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluation into .github/skills/recsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-artifact-evaluation", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install brycewang-stanford/Awesome-Journal-Skills recsys-artifact-evaluation --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/RecSys-Skills/skills/recsys-artifact-evaluation .opencode/skills/recsys-artifact-evaluation && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "recsys-artifact-evaluation" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/RecSys-Skills/skills/recsys-artifact-evaluation into .opencode/skills/recsys-artifact-evaluation/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "recsys-artifact-evaluation", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
recsys-artifact-evaluation- GitHub stars
- 1.2k
- Token cost
- ~1.1k tokens
- SKILL.md length
- 412 words
- Files
- 1
- Skills in repo
- 2,387
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after…
- Packaging ACM RecSys code
- SKILL.md covers Artifact plan, What RecSys evidence reviewers…, Worked vignette: packaging an… and Calibration anchors, plus 1 more section
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Propensity logs
What it does
Recsys Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜ 覆盖主流期刊的… The licence is MIT.
When your agent uses it
- Packaging ACM RecSys code
- Propensity logs
- Seeds as an anonymous in-paper repository during review
- A public archive after acceptance
Example prompts
- “/recsys-artifact-evaluation”
What it can do on your machine
Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Recsys Artifact Evaluation loads about 1.1k tokens when it runs. Until then it costs about 93 tokens; SKILL.md has 412 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 412 words, ~1,062 tokens.
Download SKILL.mdSave it as
.claude/skills/recsys-artifact-evaluation/SKILL.md (or your agent's skills folder).- name
- recsys-artifact-evaluation
- description
- Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.
RecSys Artifact Evaluation
Use this for evidence packaging around RecSys. The venue does not run a separate artifact-badge process; instead the Call for Contributions expects a link to an anonymous repository inside the paper, and reproducibility-minded reviewers judge the paper partly by whether that repository makes the ranking claims regenerable.
Artifact plan
- Decide what a reviewer needs to trust the claim: the exact dataset version, the split script, baseline configurations, tuning grids, trained-model checkpoints, exposure/propensity logs for off-policy claims, seeds, and the evaluation code.
- Keep decision-critical evidence in the paper or appendix; optional run files live in the repository, because RecSys reviewers are not obliged to open it.
- Anonymize repository history, commit authors, cluster paths, license headers, and any platform or organization names.
- Include a one-minute reproduction map: environment, dependencies, dataset download or identifier, commands, expected ranking numbers, runtime, seeds, and known nondeterminism.
- For proprietary interaction data, give enough provenance and preprocessing detail for credible reproduction on a public dataset without violating data-use terms.
- After acceptance, swap the anonymous mirror for a public, licensed, citable archive.
What RecSys evidence reviewers open first
| Claim type | First artifact inspected | Common failure caught |
|---|---|---|
| Top-N ranking gain | Split script + baseline configs | Random split leaking the future; baselines under-tuned |
| Off-policy / counterfactual result | Logged propensities + estimator code | Propensities missing, so the IPS/DR estimate cannot be recomputed |
| Sequential/session model | Data ordering + leave-one-last split | Test interactions seen during training |
| Reported metric values | The scorer and its cutoff | Sampled metrics presented as full-ranking numbers |
Because RecSys reviewers can and do re-run a small offline pipeline, make the headline ranking table regenerable with one command before polishing anything else.
Show full SKILL.md (143 more words)Show less
Worked vignette: packaging an off-policy study
A hypothetical submission proposes an exposure-corrected ranker validated offline and in a semi-synthetic simulator.
- Ship the logging policy's propensities alongside the interactions, not just the clicks, so the inverse-propensity estimate can be recomputed.
- Provide the simulator as one parameterized script so a reviewer can vary exposure strength and reward, rather than trusting a single frozen curve.
- Emit every ranking and reward table directly from logged results so the PDF numbers and the repository numbers cannot drift apart.
- State which regime the simulator satisfies the positivity assumption in and where it breaks it, since that mapping is what reproducibility-minded reviewers grade.
Calibration anchors
repo/
README.md # one-minute orientation: env, data id, one command per table
environment.yml # pinned versions of the recommender framework and deps
data/PREP.md # dataset version, split protocol (temporal), checksums
configs/ # per-model + per-baseline configs with tuning grids
run_tables.sh # regenerates Table 1..N from seeds- Assume only the README and one entry script get opened; design for that.
- Repository size limits and accepted hosting change by cycle; verify against the current submission instructions rather than a past year.
Output format
[Artifact role] anonymous in-paper repo / camera-ready public archive
[Contents] <data/splits/configs/checkpoints/propensities/seeds>
[Anonymity risks] <paths / commit authors / platform names / URLs>
[Reproduction level] turnkey / scripted / descriptive / weak
[Fixes before upload] <ordered list>© brycewang-stanford, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in RecSys-Skills/skills/recsys-artifact-evaluation of brycewang-stanford/Awesome-Journal-Skills.
Open the folder on GitHubat commit 932eb23
Compare with similar skills
Recsys Artifact Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Recsys Artifact Evaluation this skillbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| Splitting Datasetsforyourhealth111-pixel/Vibe-Skills | 3.6k | — | ~346 | Automated safety check: Pass | MIT | |
| Splitting Datasetsjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~836 | Automated safety check: Pass | MIT | |
| DatasetsArize-ai/phoenix | 12k | — | ~1.6k | Automated safety check: Pass | Custom licence | |
| Arize Evaluatorgithub/awesome-copilot | 40k | 1 repos | ~8.1k | Automated safety check: Notes | MIT | |
| Ccs Artifact Evaluationbrycewang-stanford/Awesome-Journal-Skills | 1.2k | — | ~969 | Automated safety check: Pass | MIT |
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Questions about Recsys Artifact Evaluation
What does Recsys Artifact Evaluation do?
A skill your agent uses when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after…. Recsys Artifact Evaluation is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when packaging ACM RecSys code, datasets, splits, trained models, propensity logs, and seeds as an anonymous in-paper repository during review or a public archive after acceptance, even though RecSys has no separate artifact badge — covering what recommender reviewers open first and how to make a top-N ranking table regenerable end to end.
When should I use Recsys Artifact Evaluation?
Recsys Artifact Evaluation fits situations like: packaging ACM RecSys code; propensity logs; seeds as an anonymous in-paper repository during review; A public archive after acceptance.
How do I install Recsys Artifact Evaluation in Claude Code?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a claude-code`. Or copy the skill folder (RecSys-Skills/skills/recsys-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/recsys-artifact-evaluation in your project. Claude Code loads it when a task matches its description.
How do I install Recsys Artifact Evaluation in Codex?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a codex`. Or copy the skill folder (RecSys-Skills/skills/recsys-artifact-evaluation in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/recsys-artifact-evaluation in your project. Codex loads it when a task matches its description.
Can I use Recsys Artifact Evaluation in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill recsys-artifact-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/recsys-artifact-evaluation, .gemini/skills/recsys-artifact-evaluation, .github/skills/recsys-artifact-evaluation and .opencode/skills/recsys-artifact-evaluation in your project.
What does Recsys Artifact Evaluation need to run?
SKILL.md names no scripts, command-line tools or credentials: Recsys Artifact Evaluation is instructions for the agent only.
Does Recsys Artifact Evaluation access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Recsys Artifact Evaluation safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Recsys Artifact Evaluation use?
Recsys Artifact Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Recsys Artifact Evaluation use?
About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Recsys Artifact Evaluation?
Skills that share tags, products or a category with Recsys Artifact Evaluation: Splitting Datasets (foryourhealth111-pixel/Vibe-Skills, 3.6k stars), Splitting Datasets (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Datasets (Arize-ai/phoenix, 12k stars) and Arize Evaluator (github/awesome-copilot, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Recsys Artifact Evaluation?
brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,231 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.
Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.